Discover Awesome MCP Servers

Veo 3.1 MCP Server

Enables high-quality AI video generation using Google's Veo 3.1 model for text-to-video, style-guided, and frame-interpolation tasks. It features token-efficient reference image handling, batch processing, and video extension capabilities with built-in cost estimation.

MCP Book Search Server

An intelligent book search server that leverages Aladin Book API and OpenAI GPT to transform natural language search requests into structured search parameters for finding relevant books.

MCP Server Demo

A demonstration MCP server that provides calculator tools for arithmetic operations, personalized greeting resources, and code review prompt templates. Enables users to perform basic math calculations, generate dynamic greetings, and access reusable code review templates through the Model Context Protocol.

Google Drive MCP Server

Enables AI agents to efficiently access and process Google Drive documents by converting them to markdown and offering selective content extraction. Features intelligent caching, advanced search capabilities, and respect for Drive permissions to optimize context window usage.

mcp-test

Okay, here are some general concepts and a basic outline for integrating an MCP (presumably referring to a Minecraft Protocol) server with Large Language Model (LLM) applications. This is a complex topic, and the specifics will depend heavily on your goals and the specific LLM and Minecraft server you're using. **Understanding the Core Concepts** * **Minecraft Protocol (MCP):** This is the communication protocol used between Minecraft clients and servers. It's a binary protocol, meaning it's not human-readable. You'll need libraries to encode and decode these packets. * **LLMs (Large Language Models):** These are AI models trained on massive datasets of text and code. They can generate text, translate languages, answer questions, and more. Examples include GPT-3/GPT-4, LaMDA, and open-source models like Llama 2. * **Integration Goal:** The goal is to allow the LLM to interact with the Minecraft server in some way. This could involve: * **Reading Server State:** Getting information about the world, players, entities, etc. * **Sending Commands:** Having the LLM issue commands to the server (e.g., `/say`, `/tp`, `/give`). * **Responding to Events:** Having the LLM react to events happening in the game (e.g., a player joining, a block being broken, a chat message being sent). * **Generating Content:** Using the LLM to create stories, quests, or even build structures within the game. **High-Level Architecture** The typical architecture involves these components: 1. **Minecraft Server:** The standard Minecraft server (e.g., Vanilla, Spigot, Paper). 2. **MCP Library/Wrapper:** A library that handles the Minecraft Protocol. This is crucial for encoding and decoding packets. Examples: * **Python:** `mcstatus`, `nbt`, `minecraft-protocol` * **Java:** `minecraft-server-util`, `netty` (for low-level protocol handling) * **Node.js:** `minecraft-protocol` 3. **Bridge/Middleware:** This is the code that connects the Minecraft server and the LLM. It typically does the following: * **Listens for Events:** Monitors the Minecraft server for relevant events (e.g., chat messages, player actions). * **Translates Data:** Converts data from the Minecraft Protocol into a format suitable for the LLM (e.g., text strings, JSON). * **Sends Data to LLM:** Sends the data to the LLM API. * **Receives LLM Response:** Gets the response from the LLM. * **Translates LLM Response:** Converts the LLM's response into Minecraft commands or actions. * **Sends Commands to Server:** Sends the commands to the Minecraft server using the MCP library. 4. **LLM API:** The API for the Large Language Model you're using (e.g., OpenAI API, Cohere API, a locally hosted LLM). **Basic Steps for Integration** 1. **Choose a Programming Language and Libraries:** Select a language (Python, Java, Node.js are common) and the appropriate MCP library. Consider the ease of use, performance, and available documentation. 2. **Set Up Minecraft Server:** Install and configure your Minecraft server. Make sure you can connect to it. 3. **Implement MCP Connection:** Write code to connect to the Minecraft server using your chosen MCP library. You'll need the server's IP address and port. 4. **Event Handling:** Implement code to listen for events on the Minecraft server. Start with a simple event like chat messages. Parse the MCP packets to extract the relevant information (e.g., the player's name and the message). 5. **LLM API Integration:** * **Get API Key:** Obtain an API key from your chosen LLM provider (e.g., OpenAI). * **Install LLM Library:** Install the appropriate library for interacting with the LLM API (e.g., `openai` for Python). * **Send Data to LLM:** Send the relevant data (e.g., the chat message) to the LLM API. You'll need to format the data as a prompt. 6. **Process LLM Response:** Receive the response from the LLM. This will typically be a text string. Parse the response to extract the desired information or command. 7. **Send Commands to Minecraft Server:** Use the MCP library to send commands to the Minecraft server based on the LLM's response. For example, if the LLM responds with "`/say Hello, world!`", you would send the appropriate MCP packet to execute that command. 8. **Error Handling:** Implement robust error handling to catch exceptions and prevent your application from crashing. **Example Scenario: LLM Responding to Chat Messages** Let's say you want the LLM to respond to chat messages in the game. 1. **Player types:** "What is the meaning of life?" 2. **Bridge:** * Receives the chat message event. * Extracts the player's name and the message. * Formats the message as a prompt for the LLM: `"A player named [player_name] asked: What is the meaning of life?"` * Sends the prompt to the LLM API. 3. **LLM:** Processes the prompt and generates a response (e.g., "The meaning of life is 42."). 4. **Bridge:** * Receives the LLM's response. * Formats the response as a Minecraft command: `"/say [LLM] The meaning of life is 42."` * Sends the command to the Minecraft server. 5. **Minecraft Server:** Executes the command, and the message "\[LLM] The meaning of life is 42." is displayed in the chat. **Code Snippet (Python - Conceptual)** ```python # This is a simplified example and requires proper MCP library setup import mcstatus import openai # Minecraft server details server_address = "your_server_ip:25565" # OpenAI API key openai.api_key = "YOUR_OPENAI_API_KEY" def handle_chat_message(player_name, message): """Handles a chat message by sending it to the LLM and executing the response.""" prompt = f"A player named {player_name} said: {message}" response = openai.Completion.create( engine="text-davinci-003", # Choose an appropriate LLM engine prompt=prompt, max_tokens=50, ) llm_response = response.choices[0].text.strip() # Format the LLM response as a Minecraft command minecraft_command = f"/say [LLM] {llm_response}" # Send the command to the Minecraft server (using your MCP library) send_minecraft_command(minecraft_command) # Replace with actual MCP code def send_minecraft_command(command): """Sends a command to the Minecraft server using the MCP library.""" # Replace this with your actual MCP code to send the command print(f"Sending command to Minecraft: {command}") # Example using mcstatus (not for sending commands, just for demonstration) try: server = mcstatus.MinecraftServer.lookup(server_address) status = server.status() print(f"Server latency: {status.latency}") except Exception as e: print(f"Error connecting to server: {e}") # Example usage (replace with your actual event loop) # This is just a placeholder - you need to integrate with your MCP library's event handling # to receive actual chat messages. if __name__ == "__main__": print("Starting Minecraft LLM integration...") # Simulate a chat message handle_chat_message("Player123", "Hello, world!") handle_chat_message("Player456", "What is the capital of France?") print("Integration complete.") ``` **Important Considerations** * **Security:** Be extremely careful about allowing the LLM to execute arbitrary commands on your Minecraft server. Implement strict input validation and sanitization to prevent malicious commands. Consider using a whitelist of allowed commands. * **Rate Limiting:** LLM APIs often have rate limits. Implement rate limiting in your code to avoid exceeding these limits. * **Cost:** Using LLM APIs can be expensive. Monitor your API usage and set spending limits. * **Latency:** There will be latency involved in sending data to the LLM and receiving a response. This can make the interaction feel less responsive. * **Context:** LLMs typically have a limited context window. You may need to implement a mechanism to provide the LLM with relevant context about the game world. * **Ethical Considerations:** Be mindful of the ethical implications of using LLMs in a game environment. Avoid generating content that is offensive, harmful, or misleading. * **Minecraft Server Version:** The Minecraft protocol changes with each version. Make sure your MCP library is compatible with the version of your Minecraft server. **Next Steps** 1. **Choose your MCP library:** Research and select the best library for your chosen language and Minecraft server version. 2. **Study the MCP:** Learn the basics of the Minecraft Protocol. Understand how chat messages, commands, and other events are encoded. 3. **Start Small:** Begin with a simple integration, such as having the LLM respond to chat messages. 4. **Iterate and Improve:** Gradually add more features and complexity to your integration. This is a complex project, but with careful planning and execution, you can create some very interesting and innovative applications. Good luck!

@squidlerio/mcp

An MCP proxy that connects AI clients to the remote Squidler MCP server while providing local Chrome session management for testing localhost URLs. It enables transparent tool forwarding and allows browser-based test cases to run through a local browser instead of cloud instances.

MISP MCP Server

Server Protokol Konteks Model (MCP) yang terintegrasi dengan MISP (Platform Berbagi Informasi Malware) untuk menyediakan kemampuan intelijen ancaman ke Model Bahasa Besar.

Tavily Web Search MCP Server

Enables web search capabilities through the Tavily API, allowing users to search the web for information using natural language queries. Demonstrates MCP (Model Context Protocol) server implementation with stdio transport mode.

Wordle MCP (Python)

Fetches Wordle solutions from the Wordle API for dates ranging from May 19, 2021 to 23 days in the future.

Financial MCP Server

Provides professional-grade access to financial and economic data from Yahoo Finance and the Federal Reserve Economic Data (FRED). It enables users to perform comprehensive stock analysis, screen equities, and retrieve a wide range of macroeconomic indicators and historical data.

Credit Optimizer v5

AI-powered credit optimization for Manus AI. Reduces credit consumption by 30-75% through intelligent model routing, context hygiene, and smart task processing. Audited across 53 scenarios with zero quality loss.

SuiAgentic

A FastAPI-based application that enables document embedding and semantic retrieval using Qdrant vector database, allowing users to convert documents into embeddings and retrieve relevant content through natural language queries.

Elysia MCP Plugin

A comprehensive ElysiaJS plugin for building Model Context Protocol (MCP) servers with HTTP transport, session management, and support for tools, resources, and prompts.

Flights MCP

Flights MCP

Interzoid Global Time API Server

An MCP server that allows users to get global time information via Interzoid's GetGlobalTime API, enabling access to time data across different regions through natural language.

Weather & Stock MCP Server

Menyediakan peringatan cuaca waktu nyata, perkiraan cuaca, harga saham, data historis, dan berita terkait saham melalui antarmuka MCP.

MCP Azure DevOps Server

An open-source server that enables AI agents to interact with Azure DevOps projects through the Model Context Protocol, providing tools for managing work items, wikis, and repositories to streamline development workflows.

Rowan MCP Server

Enables molecular design and simulation through 45 chemistry tools including pKa calculations, geometry optimization, conformer searches, docking, protein cofolding, and ADMET predictions powered by Rowan's computational chemistry platform.

Vibe Preprocessing and Analysis MCP Server

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

Jokes MCP Server

An MCP server implementation that delivers various joke types (Chuck Norris, Dad jokes, etc.) to Microsoft Copilot Studio and GitHub Copilot through the Model Context Protocol.

powerbi-tabular-mcp

tabular-mcp

Meeting Room MCP Server

A simple MCP server that enables meeting room booking through an AI assistant, supporting room availability checks and booking operations with React Agent pattern for tool calling.

atlas-browser-mcp

Enables AI agents to navigate the web visually using screenshot-based interaction and Set-of-Mark labeling for interactive elements. It supports humanized browsing behaviors, anti-detection measures, and complex tasks like multi-click CAPTCHA solving.

qasphere-mcp

Server QA Sphere MCP yang memungkinkan Large Language Model berinteraksi langsung dengan kasus uji sistem manajemen pengujian, mendukung alur kerja pengembangan bertenaga AI dan penemuan kasus uji.

MCP Planning Server

Provides structured task planning and tracking capabilities with 40 tools for managing requirements, solutions, architectural decisions, and hierarchical implementation phases with progress tracking.

MCP Server Tools

Provides basic utility tools including calculator for arithmetic operations, text processor for string transformations, and JSON formatter for validating and formatting JSON data.

NetBox Read/Write MCP Server

A Model Context Protocol server that provides safe, intelligent read/write access to NetBox instances, designed with safety-first principles for Large Language Model automation.

MCP Excel Server

An MCP server that enables AI agents to automate Excel file operations including workbook creation, worksheet management, and data manipulation. It provides a standardized interface for reading, writing, merging, and filtering cell ranges within Excel documents.

CodeAnalysis MCP Server

Server Protokol Konteks Model komprehensif untuk analisis kode tingkat lanjut yang menyediakan alat untuk analisis sintaksis, visualisasi dependensi, dan dukungan alur kerja pengembangan yang dibantu AI.